Disordered metabolic evaluation in renal stone recurrence : a data mining approach

Saraee, MH; Givchi, A; Taghi Adl, S; Eshraghi, A

Abstract

Nephrolithiasis is a disease with a high and even rising incidence. It has a high morbidity, generates high costs and has a high recurrence rate. Metabolic evaluation in renal stone formers allows the identification and quantification of risk factors and establishment of individual risk profiles. Based on these individuals risk profiles, rational therapy for metaphylaxis of renal stones lowers stone recurrence rate significantly. The purpose of this article is metabolic investigation in patients with nephrolithiasis in Isfahan city- Iran. Different data mining algorithms such as Clustering and Classification were employed for extracting knowledge in the form of decision rules. These results evaluate the risk of morbidity and recurrence of the diseases.Some medical attributes gathered based on their medical importance. The data mining tasks applied in this research have been applied and tested over 406 observed samples collected at different clinics in the city of Isfahan.

Citation

Saraee, M., Givchi, A., Taghi Adl, S., & Eshraghi, A. (2011). Disordered metabolic evaluation in renal stone recurrence : a data mining approach. Journal of Applied Computer Science & Mathematics (Suceava. Online), 5(11), 64-68

• Journal Article Type: Article
• Publication Date: Jan 1, 2011
• Deposit Date: Jul 12, 2017
• Journal: Applied Computer Science & Mathematics
• Print ISSN: 1843-1046
• Electronic ISSN: 2066-3129
• Volume: 5
• Issue: 11
• Pages: 64-68
• Publisher URL: https://doaj.org/article/cfa1d968a22a49ecab2d7aa24c5321cd
• Related Public URLs: http://www.usv.ro/index.php/en